# Data Analytics Fundamentals

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## 18 Questions

### What is the primary purpose of data analytics in commercial industries?

To make business decisions and optimize performance

Data Analysis

### What type of analytics involves using data to predict future outcomes?

Predictive analytics

### Who are responsible for performing data analytics tasks?

Data Analysts and Data Engineers

### What is the term for the process of transforming data into a more usable format?

Data Transformation

### What is the primary goal of data visualization?

To get better perception of datasets

### What is a crucial requirement for a scale in data visualization?

It must be a one-to-one mapping

### What type of scale is suitable for data that exhibits exponential growth?

Logarithmic scale

### What is the purpose of a scale in data visualization?

To map data values onto aesthetic values

### What type of coordinate system is commonly used for circular data?

Polar coordinate system

Circular

### What is the result of using a scale that is not one-to-one?

A more ambiguous data visualization

### Which type of data analysis involves understanding the relationships between variables?

Multivariate analysis

### What is an example of a data transformation?

Applying a logarithmic scale to the data

### What is the purpose of data transformation in the context of data analysis?

To stabilize the variance of continuous data

### What is the primary advantage of using logarithmic scales in data visualization?

They can be used to visualize nonlinear relationships

### What is the main difference between categorical and continuous data?

Categorical data is qualitative, while continuous data is quantitative

### What is the coordinate system used to specify positions via an angle and a radial distance from the origin?

Polar coordinate system

## Study Notes

### Data Types and Visualization

• A dataset can have various types of data, including:
• Ordered factor (e.g., month)
• Discrete numerical value (e.g., day)
• Unordered factor (e.g., location, station ID)
• Continuous numerical value (e.g., temperature in degrees Fahrenheit)

### Data Visualization and Scales

• Scales map data values to aesthetic values (e.g., x-axis positions, shapes, colors)
• A scale must be one-to-one, ensuring a unique mapping between data and aesthetics values
• In polar coordinate systems, positions are specified via an angle and radial distance from the origin

• Importing datasets and libraries (e.g., numpy, pandas, matplotlib, seaborn)
• Importing a dataset (e.g., Credit Card Approvals) using pd.read_csv()

### Data Characteristics

• Categorical data: Gender, Married, BankCustomer, Industry, Ethnicity, PriorDefault, Employed, DrivingLicense, Citizen, Approved
• Continuous data: Age, Debt, YearsEmployed, CreditScore, Income

### Univariate Analysis

• Univariate analysis is a basic form of data analysis, focusing on a single variable
• Used to understand the data without examining causes or effects relationships

### Data Analytics

• Data analytics: the process of analyzing data sets to make informed decisions
• Uses specialized software and systems to help businesses:
• Understand customers better
• Personalize content
• Improve bottom lines

### Data Visualization and Analytics

• Data visualization helps gain better insights into data
• Both data visualization and analytics draw conclusions about datasets
• Data visualization can be static or interactive

Test your understanding of data analytics, its applications, and techniques in commercial industries. Learn how data analytics can help businesses make informed decisions, improve customer understanding, and boost their bottom lines.

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